TY - GEN
T1 - Visual-based Real Time Driver Drowsiness Detection System Using CNN
AU - Flores-Monroy, Jonathan
AU - Nakano-Miyatake, Mariko
AU - Sanchez-Perez, Gabriel
AU - Perez-Meana, Hector
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The traffic accident is one of the most frequent cause of death in the world; and an important cause of the traffic accident is the fatigue of the driver, who falls asleep during driving. To overcome this problem in this paper, we propose a real-Time driver drowsiness detection system, in which the driver's face region is extracted and introduced into a specific designed shallow convolutional neural network (SS-CNN). The SS-CNN detects the state of driver drowsiness using 'eye closure' or 'eye open' state. To distinguish between the 'eye closed' state caused by normal eye blinking and that caused by drowsiness, the proposed system analyzes consecutive results of the SS-CNN. If the system determines that driver falls asleep, an alarm rings to awake the driver in order to avoid a possible accident. The proposed SS-CNN provides an accuracy of 98.95%, which outperforms the previous works. In the experimental section, we provide several links in which real-Time operations of the proposed system can be observed.
AB - The traffic accident is one of the most frequent cause of death in the world; and an important cause of the traffic accident is the fatigue of the driver, who falls asleep during driving. To overcome this problem in this paper, we propose a real-Time driver drowsiness detection system, in which the driver's face region is extracted and introduced into a specific designed shallow convolutional neural network (SS-CNN). The SS-CNN detects the state of driver drowsiness using 'eye closure' or 'eye open' state. To distinguish between the 'eye closed' state caused by normal eye blinking and that caused by drowsiness, the proposed system analyzes consecutive results of the SS-CNN. If the system determines that driver falls asleep, an alarm rings to awake the driver in order to avoid a possible accident. The proposed SS-CNN provides an accuracy of 98.95%, which outperforms the previous works. In the experimental section, we provide several links in which real-Time operations of the proposed system can be observed.
KW - Convolutional neural Networks
KW - Real time implementation
KW - Visual detection
KW - driver's drowsiness detection
KW - driver's fatigue
UR - http://www.scopus.com/inward/record.url?scp=85123842831&partnerID=8YFLogxK
U2 - 10.1109/CCE53527.2021.9633082
DO - 10.1109/CCE53527.2021.9633082
M3 - Contribución a la conferencia
AN - SCOPUS:85123842831
T3 - CCE 2021 - 2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control
BT - CCE 2021 - 2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2021
Y2 - 10 November 2021 through 12 November 2021
ER -
